Package nflgame
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Source Code for Package nflgame

  1  """ 
  2  Introduction 
  3  ============ 
  4  An API to retrieve and read NFL Game Center JSON data. 
  5  It can work with real-time data, which can be used for fantasy football. 
  6   
  7  nflgame works by parsing the same JSON data that powers NFL.com's live 
  8  GameCenter. Therefore, nflgame can be used to report game statistics while 
  9  a game is being played. 
 10   
 11  The package comes pre-loaded with game data from every pre- and regular 
 12  season game from 2009 up until August 28, 2012. Therefore, querying such data 
 13  does not actually ping NFL.com. 
 14   
 15  However, if you try to search for data in a game that is being currently 
 16  played, the JSON data will be downloaded from NFL.com at each request (so be 
 17  careful not to inspect for data too many times while a game is being played). 
 18  If you ask for data for a particular game that hasn't been cached to disk 
 19  but is no longer being played, it will be automatically cached to disk 
 20  so that no further downloads are required. 
 21   
 22  nflgame requires Python 2.6 or Python 2.7. It does not (yet) work with 
 23  Python 3. 
 24   
 25  Examples 
 26  ======== 
 27   
 28  Finding games 
 29  ------------- 
 30  Games can be selected in bulk, e.g., every game in week 1 of 2010:: 
 31   
 32      games = nflgame.games(2010, week=1) 
 33   
 34  Or pin-pointed exactly, e.g., the Patriots week 17 whomping against the Bills:: 
 35   
 36      game = nflgame.game(2011, 17, "NE", "BUF") 
 37   
 38  This season's (2012) pre-season games can also be accessed:: 
 39   
 40      pregames = nflgame.games(2012, preseason=True) 
 41   
 42  Find passing leaders of a game 
 43  ------------------------------ 
 44  Given some game, the player statistics can be easily searched. For example, 
 45  to find the passing leaders of a particular game:: 
 46   
 47      for p in game.players.passing().sort("passing_yds"): 
 48          print p, p.passing_att, p.passing_cmp, p.passing_yds, p.passing_tds 
 49   
 50  Output:: 
 51   
 52      T.Brady 35 23 338 3 
 53      R.Fitzpatrick 46 29 307 2 
 54      B.Hoyer 1 1 22 0 
 55   
 56  See every player that made an interception 
 57  ------------------------------------------ 
 58  We can filter all players on whether they had more than zero defensive 
 59  interceptions, and then sort those players by the number of picks:: 
 60   
 61      for p in game.players.filter(defense_int=lambda x:x>0).sort("defense_int"): 
 62          print p, p.defense_int 
 63   
 64  Output:: 
 65   
 66      S.Moore 2 
 67      A.Molden 1 
 68      D.McCourty 1 
 69      N.Barnett 1 
 70   
 71  Finding weekly rushing leaders 
 72  ------------------------------ 
 73  Sequences of players can be added together, and their sum can then be used 
 74  like any other sequence of players. For example, to get every player 
 75  that played in week 10 of 2009:: 
 76   
 77      week10 = nflgame.games(2009, 10) 
 78      players = nflgame.combine(week10) 
 79   
 80  And then to list all rushers with at least 10 carries sorted by rushing yards:: 
 81   
 82      rushers = players.rushing() 
 83      for p in rushers.filter(rushing_att=lambda x: x > 10).sort("rushing_yds"): 
 84          print p, p.rushing_att, p.rushing_yds, p.rushing_tds 
 85   
 86  And the final output:: 
 87   
 88      A.Peterson 18 133 2 
 89      C.Johnson 26 132 2 
 90      S.Jackson 26 131 1 
 91      M.Jones-Drew 24 123 1 
 92      J.Forsett 17 123 1 
 93      M.Bush 14 119 0 
 94      L.Betts 26 114 1 
 95      F.Gore 25 104 1 
 96      J.Charles 18 103 1 
 97      R.Williams 20 102 0 
 98      K.Moreno 18 97 0 
 99      L.Tomlinson 24 96 2 
100      D.Williams 19 92 0 
101      R.Rice 20 89 1 
102      C.Wells 16 85 2 
103      J.Stewart 11 82 2 
104      R.Brown 12 82 1 
105      R.Grant 19 79 0 
106      K.Faulk 12 79 0 
107      T.Jones 21 77 1 
108      J.Snelling 18 61 1 
109      K.Smith 12 55 0 
110      C.Williams 14 52 1 
111      M.Forte 20 41 0 
112      P.Thomas 11 37 0 
113      R.Mendenhall 13 36 0 
114      W.McGahee 13 35 0 
115      B.Scott 13 33 0 
116      L.Maroney 13 31 1 
117   
118  You could do the same for the entire 2009 season:: 
119   
120      players = nflgame.combine(nflgame.games(2009)) 
121      for p in players.rushing().sort("rushing_yds").limit(35): 
122          print p, p.rushing_att, p.rushing_yds, p.rushing_tds 
123   
124  And the output:: 
125   
126      C.Johnson 322 1872 12 
127      S.Jackson 305 1361 4 
128      A.Peterson 306 1335 17 
129      T.Jones 305 1324 12 
130      M.Jones-Drew 296 1309 15 
131      R.Rice 240 1269 7 
132      R.Grant 271 1202 10 
133      C.Benson 272 1118 6 
134      D.Williams 210 1104 7 
135      R.Williams 229 1090 11 
136      R.Mendenhall 222 1014 7 
137      F.Gore 206 1013 8 
138      J.Stewart 205 1008 9 
139      K.Moreno 233 897 5 
140      M.Turner 177 864 10 
141      J.Charles 165 861 5 
142      F.Jackson 205 850 2 
143      M.Barber 200 841 7 
144      B.Jacobs 218 834 5 
145      M.Forte 242 828 4 
146      J.Addai 213 788 9 
147      C.Williams 190 776 4 
148      C.Wells 170 774 7 
149      A.Bradshaw 156 765 7 
150      L.Maroney 189 735 9 
151      J.Harrison 161 735 4 
152      P.Thomas 141 733 5 
153      L.Tomlinson 221 729 12 
154      Kv.Smith 196 678 4 
155      L.McCoy 154 633 4 
156      M.Bell 155 626 5 
157      C.Buckhalter 114 624 1 
158      J.Jones 163 602 2 
159      F.Jones 101 594 2 
160      T.Hightower 137 574 8 
161   
162  Load data into Excel 
163  -------------------- 
164  Every sequence of Players can be easily dumped into a file formatted 
165  as comma-separated values (CSV). CSV files can then be opened directly 
166  with programs like Excel, Google Docs, Open Office and Libre Office. 
167   
168  You could dump every statistic from a game like so:: 
169   
170      game.players.csv('player-stats.csv') 
171   
172  Or if you want to get crazy, you could dump the statistics of every player 
173  from an entire season:: 
174   
175      nflgame.combine(nflgame.games(2010)).csv('season2010.csv') 
176  """ 
177   
178  try: 
179      from collections import OrderedDict 
180  except: 
181      from ordereddict import OrderedDict  # from PyPI 
182   
183  import nflgame.game as game 
184  import nflgame.player as player 
185  import nflgame.schedule as schedule 
186   
187  VERSION = "1.0.6" 
188   
189  NoPlayers = player.Players(None) 
190  """ 
191  NoPlayers corresponds to the identity element of a Players sequences. 
192   
193  Namely, adding it to any other Players sequence has no effect. 
194  """ 
195   
196   
197 -def games(year, week=None, home=None, away=None, preseason=False):
198 """ 199 games returns a list of all games matching the given criteria. Each 200 game can then be queried for player statistics and information about 201 the game itself (score, winner, scoring plays, etc.). 202 203 Note that if a game's JSON data is not cached to disk, it is retrieved 204 from the NFL web site. A game's JSON data is *only* cached to disk once 205 the game is over, so be careful with the number of times you call this 206 while a game is going on. (i.e., don't piss off NFL.com.) 207 """ 208 eids = __search_schedule(year, week, home, away, preseason) 209 if not eids: 210 return None 211 return [game.Game(eid) for eid in eids]
212 213
214 -def one(year, week, home, away, preseason=False):
215 """ 216 one returns a single game matching the given criteria. The 217 game can then be queried for player statistics and information about 218 the game itself (score, winner, scoring plays, etc.). 219 220 one returns either a single game or no games. If there are multiple games 221 matching the given criteria, an assertion is raised. 222 223 Note that if a game's JSON data is not cached to disk, it is retrieved 224 from the NFL web site. A game's JSON data is *only* cached to disk once 225 the game is over, so be careful with the number of times you call this 226 while a game is going on. (i.e., don't piss off NFL.com.) 227 """ 228 eids = __search_schedule(year, week, home, away, preseason) 229 if not eids: 230 return None 231 assert len(eids) == 1, 'More than one game matches the given criteria.' 232 return game.Game(eids[0])
233 234
235 -def combine(games):
236 """ 237 Combines a list of games into one big player sequence. 238 239 This can be used, for example, to get Player objects corresponding to 240 statistics across an entire week, some number of weeks or an entire season. 241 """ 242 players = OrderedDict() 243 for game in games: 244 for p in game.players: 245 if p.playerid not in players: 246 players[p.playerid] = p 247 else: 248 players[p.playerid] += p 249 return player.Players(players)
250 251
252 -def __search_schedule(year, week=None, home=None, away=None, preseason=False):
253 """ 254 Searches the schedule to find the game identifiers matching the criteria 255 given. 256 """ 257 ids = [] 258 for (y, t, w, h, a), info in schedule.games: 259 if y != year: 260 continue 261 if week is not None: 262 if isinstance(week, list) and w not in week: 263 continue 264 if not isinstance(week, list) and w != week: 265 continue 266 if home is not None and h != home: 267 continue 268 if away is not None and a != away: 269 continue 270 if preseason and t != "PRE": 271 continue 272 if not preseason and t != "REG": 273 continue 274 ids.append(info['eid']) 275 return ids
276